臺灣博碩士論文加值系統

臨床診斷超音波脂肪肝影像具主觀性診斷，目前電腦輔助診斷系統(Computer-Aided Diagnosis, CAD)分析脂肪肝影像分類需要手動預處理分割並進行特徵萃取。近年利用人工智慧辨識影像類別，已證實具有合理性及前瞻性。因此本研究採用小樣本下具有良好表現之ResNet進行脂肪肝影像分類。
本研究採用回顧性分組實驗設計，蒐集2016/01/01至2017/11/30腹部B-mode超音波肝臟影像，共計416筆正常肝臟案例及311筆脂肪肝案例；脂肪肝分成三類包括輕度(n=108)、中度(n=101)、重度(n=102)。採用ResNe 演算法建立正常與非正常、輕度與非輕度(即,中度與重度脂肪肝影像)、中度與重度共三組狀模型(Nested Model)；其中訓練組、驗證組及測試組各佔比為70%、20%、10%；模型評估採用測試組之分類準確度、特異性、靈敏度及Kappa一致性統計量。
測試組結果顯示正常與非正常超音波肝臟影像分類結果最佳準確度為91.7%；輕度與非輕度脂肪肝影像分類結果最佳準確度為90.3%；中度與重度脂肪肝影像分類最佳結果準確度為95%，三組ResNet分類都具有趨近90%的準確度；巢狀模型對四種分類準確度為70%；四組分類結果之Kappa一致性統計量為0.6，表示分類結果具有中度一致性。
本研究針對正常、輕度、中度與重度分類表現準確度具有合理性；未來可以探討不同人工智慧影像辨識模型如DenseNet、NASNet-Mobile、NASNet-Large等，期能提升腹部B-mode超音波脂肪肝影像預測分類可靠性及穩定性。

In clinics, the diagnosis of fatty liver via ultrasound images is subjectively. Meanwhile, Computer-Aided Diagnosis (CAD) was adopted to classify stages of fatty liver through manually pre-processing and extracting features of images. Now a day, the artificial intelligent with deep learning algorithms are developed to identify or classify the categories of image. In this study, the ResNet, performed with small sample, was utilized to classify the stages of fatty liver.
A retrospective designed with multiple groups were enrolled in this study. The liver ultrasound images were collected from 2016/01/01 to 2017/12/31. Total of 416 and 311 cases were normal and fatty liver respectively. There were three categories of fatty liver, including mild (n=108), moderate (n=101) and severe (n=102) groups. The ResNet algorithm was used to construct a nested model which was combined three binary classification models. One was for classification between normal and abnormal. Second was for classification between mild and non-mild fatty liver. Third was for classification between moderate and severe fatty liver. The percentage of training, validation and testing set were 70%, 20% and 10% of overall samples. The classified performance was evaluated by the accuracy, specificity and sensitivity on testing set. The Kappa statistics was applied to exam the agreement of classified results with supervised diagnostic judgments.
The accuracy among three binary classified models were 91.7%, 90.3%, and 95% with respectively to normal and abnormal, mild and non-mild, and moderate and severe groups. The accuracy of classified 4 groups via presented nested model was 70%. Meanwhile, the Kappa statistics provided by presented nested model was 0.6 which was moderate agreement with clinical judgments.
The presented model was demonstrated feasibility for classified 4 groups. In the future, the DenseNet, NASNet-Mobile, and NASNet-Large might be adopted to create classified models for liver ultrasound tomography with expect to improve the reliability and stability.

摘 要 I
Abstract II
誌謝 III
目錄 IV
圖目錄 VI
第一章 緒論 1
1.1前言 1
1.2超音波成像基礎原理 5
1.3脂肪肝 7
1.4臨床脂肪肝超音波影像的簡易判讀 9
1.5研究動機與目的 9
第二章 文獻探討 11
第三章 研究材料與方法 16
3.1研究資料來源 16
3.2造影儀器及條件 16
3.3研究方法與流程 18
3.4驗證方法 29
第四章 結果 32
第五章 結論與討論 35
5.1結論 35
5.2討論 35
5.3未來研究方向 36
5.4研究限制 36
參考文獻 37
附件: 附-1
附件1、人體研究計畫同意函 附件1

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